本論文設計一個生理訊號放大電路，其電路可量測多種生理訊號。藉由一個3通道之人機介面系統和判斷規則演算法，分別可以量測眼動水平訊號、垂直訊號與腦波訊號(α波)，藉此偵測眼球上、下、左、右移動、自主性眨眼，或大腦是否產生α波。本論文將此3通道之人機介面系統實際應用於多方面之情境，首先，設計一個人機介面輔助系統來模擬滑鼠的操作，讓幫助身障人士可以透過眼球轉動方向與眨眼的動作來操作電腦。接著，也嘗試將此人機介面應用於睡眠的快速動眼期(Rapid Eye Movement, REM)分析和直升機之遙控操作。快速動眼期的分析是透過傅利葉轉換(Fast Fourier Transform, FFT)、轉折點(Turn counts)、越零率 (Zero-crossing rate, ZCR)演算法來分析睡眠時快速動眼期出現的時間。遙控直升機系統則是透過偵測眼球的轉動來控制直升機行進方向，而α波則是用來操控直升機的起飛與降落。最後透過實驗來驗證此系統之有效性，在人機介面的實驗方面，本論文偵測使用者6種動作上、下、左、右、自主性眨眼、與α波，其辨識率約八成五。 睡眠分析透過三種演算法判斷可正確標示出快速動眼期位置。最後遙控直升機系統的操作，透過眼球方向與α波的訊號，可控制直升機遙控器來操作飛機。

This thesis presents a physiological signal processing circuit which can be used to measure many kinds of physiological signals. Based on this circuit, a 3-channel human-computer interface (HCI) system incorporated with a decision rule algorithm is implemented to measure vertical and horizontal eye movements, and alpha waves of brain signals. The 3-channel human-computer interface (HCI) system can be used in three different application domains. First of all, the system is utilized to be a computer interface for the disabled persons. The user can use his or her eye movements to control the mouse and then operate a communication aid for communications, typing, web surfing, and controlling home appliances. Secondly, the system incorporated with an algorithm is utilized to be a tool for recording and detecting the Rapid Eye Movement (REM) events during a sleep period. REM events are detected via the features extracted from the Fast Fourier Transform (FFT), turn counts, and zero-crossing rate (ZCR). The system is also used to control a toy helicopter. The moving directions are controlled by the eye movements and the start/stop is controlled by the alpha waves. Several experiments were designed to evaluate the system. The recognition rate for classifying the eye movements was about 85% ratio correct. Experimental results also shows the system can correctly detect the REM events and control a toy helicopter.

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